This entry contains all the files required to implement face-domain-specific automatic speech recognition (ASR) applications using the Kaldi ASR toolkit (https://github.com/kaldi-asr/kaldi), including the acoustic model, language model, and other relevant files. It also includes all the scripts and configuration files needed to use these models for implementing face-domain-specific automatic speech recognition. The acoustic model was trained using the relevant Kaldi ASR tools (https://github.com/kaldi-asr/kaldi) and the Artur speech corpus (http://hdl.handle.net/11356/1776; http://hdl.handle.net/11356/1772). The language model was trained using the domain-specific text data involving face descriptions obtained by translating the Face2Text English dataset (https://github.com/mtanti/face2text-dataset) into the Slovenian language. These models, combined with other necessary files like the HCLG.fst and decoding scripts, enable the implementation of face-domain-specific ASR applications. Two speech corpora ("test" and "obrazi") and two Kaldi ASR models ("graph_splosni" and "graph_obrazi") can be selected for conducting speech recognition tests by setting the variable "graph" and "test_sets" in the "local/test_recognition.sh" script. Acoustic speech features can be extracted and speech recognition tests can be conducted using the "local/test_recognition.sh" script. Speech recognition test results can be obtained using the "results.sh" script. The KALDI_ROOT environment variable also needs to be set in the script "path.sh" to set the path to the Kaldi ASR toolkit installation folder.

Introduction

Introducing the UK English Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of English language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in UK English, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

ARTUR is a speech database designed for the needs of automatic speech recognition for the Slovenian language. The database includes 1,035 hours of speech, although only 840 hours are transcribed, while the remaining 195 hours are without transcription. The data is divided into 4 parts: (1) approx. 520 hours of read speech, which includes the reading of pre-defined sentences, selected from the corpus Gigafida; each sentence is contained in one file; speakers are demographically balanced; spelling is included in special files; all with manual transcriptions; (2) approx. 204 hours of public speech, which includes media recordings, online recordings of conferences, workshops, education videos, etc.; 56 hours are manually transcribed; (3) approx. 110 hours of private speech, which includes monologues and dialogues between two persons, recorded for the purposes of the speech database; the speakers are demographically balanced; two subsets for domain-specific ASR (i.e., smart-home and face-description) are included; 63 hours are manually transcribed; (4) approx. 201 hours of parliamentary speech, which includes recordings from the Slovene National Assembly, all with manual transcriptions. This repository entry includes transcriptions in Transcriber 1.5.1 TRS format only; audio recordings are available at http://hdl.handle.net/11356/1717.

Automatic Speech Recognition Software Market Outlook

According to our latest research, the global automatic speech recognition (ASR) software market size reached USD 10.8 billion in 2024, driven by rapid advancements in artificial intelligence and machine learning technologies. The market is expected to witness robust expansion, registering a CAGR of 19.2% from 2025 to 2033. By the end of the forecast period in 2033, the global ASR software market is anticipated to attain a value of USD 47.8 billion. The key growth factor propelling this market is the increasing integration of voice-enabled technologies across diverse industries to enhance user experience, operational efficiency, and accessibility.

The surge in demand for contactless interfaces, especially post-pandemic, has significantly accelerated the adoption of automatic speech recognition software across several sectors. Enterprises are increasingly leveraging ASR solutions to streamline workflows, reduce manual intervention, and improve accuracy in data entry and customer service. The proliferation of smart devices, virtual assistants, and IoT ecosystems has further fueled the necessity for sophisticated speech recognition capabilities. Additionally, advancements in natural language processing (NLP) and deep learning algorithms have markedly improved the accuracy and versatility of ASR systems, making them viable for complex, multilingual, and domain-specific applications.

Another pivotal growth driver is the growing emphasis on accessibility and inclusivity in digital services. Governments and regulatory bodies worldwide are mandating organizations to provide accessible digital content, especially for individuals with disabilities. ASR software plays a crucial role in enabling real-time transcription, voice commands, and automated captioning, thereby fostering digital inclusion. The healthcare sector, in particular, has witnessed a surge in ASR adoption for clinical documentation, telemedicine, and virtual consultations, reducing administrative burdens and enhancing patient care outcomes. Furthermore, the education sector has embraced ASR for lecture transcription and language learning, broadening its reach and impact.

The increasing prevalence of remote work and virtual collaboration tools has also contributed to the rapid growth of the automatic speech recognition software market. Enterprises are deploying ASR solutions to facilitate seamless meeting transcriptions, real-time translations, and voice-driven workflows, thereby boosting productivity and collaboration across geographically dispersed teams. The integration of ASR with customer relationship management (CRM) and enterprise resource planning (ERP) systems is further streamlining business operations and enabling data-driven decision-making. These factors, coupled with the declining cost of cloud computing and storage, are making ASR solutions more accessible to small and medium-sized enterprises (SMEs), thereby expanding the marketÂ’s user base.

The telecom industry is undergoing a transformative phase with the integration of Speech Recognition in Telecom, which is enhancing customer interactions and operational efficiencies. By deploying ASR technology, telecom companies are able to offer voice-driven services that cater to the needs of a diverse customer base. This includes automated customer support, voice-activated service menus, and enhanced call routing, which significantly reduce wait times and improve customer satisfaction. Moreover, the ability to analyze customer sentiment and preferences through voice data is enabling telecom providers to tailor their offerings and marketing strategies more effectively. This technological advancement is not only streamlining customer service operations but also paving the way for innovative applications in areas like fraud detection and network management.

From a regional perspective, North America continues to dominate the ASR software market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership can be attributed to the presence of major technology vendors, early adoption of AI-driven solutions, and robust investments in R&D. However, the Asia Pacific region is poised to exhibit the fastest growth during the forecast period, driven by rapid digit

Introduction

This Odia Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Odia speech recognition, spoken language understanding, and conversational AI systems. With 40 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 40 Hours of dual-channel call center conversations between native Odia speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Multi-domain academic audio data for evaluating ASR model

  Dataset Summary

This dataset, named "DomainSpeech," is meticulously curated to serve as a robust evaluation tool for Automatic Speech Recognition (ASR) models. Encompassing a broad spectrum of academic domains including Agriculture, Sciences, Engineering, and Business. A distinctive feature of this dataset is its deliberate design to present a more challenging benchmark by maintaining a technical terminology… See the full description on the dataset page: https://huggingface.co/datasets/AcaSp/DomainSpeech.

Introduction

Introducing the Bahasa Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of Bahasa language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in Bahasa, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

This Korean Financial Speech Dataset contains 215 hours of real-world audio, including casual conversations and monologues. The content spans professional financial terminology in macroeconomics and microeconomics contexts, simulating authentic banking and financial service interactions. Each recording includes transcriptions, speaker metadata (ID, gender), and tagged financial entities. The dataset supports a wide range of AI applications such as automatic speech recognition (ASR), financial natural language understanding (NLU), voicebot development, and domain-specific language modeling. All data complies with GDPR, CCPA, and PIPL regulations, ensuring privacy and ethical usage.

Introduction

This Japanese Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Japanese speech recognition, spoken language understanding, and conversational AI systems. With 40 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 40 Hours of dual-channel call center conversations between native Japanese speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

FLEURS

Fleurs is the speech version of the FLoRes machine translation benchmark. We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven… See the full description on the dataset page: https://huggingface.co/datasets/google/fleurs.

Tilde has worked on spoken language processing since the late 1990s. The special attention is paid to data sparseness problem that is typical for morphologically rich languages and to novel methods for data acquisition from the web. Tilde continues research on speech recognition by adapting developed technologies for new languages and for specific domains.

The database actually contains two sets of recordings, both recorded in the moving or stationary vehicles (passenger cars or trucks). All data were recorded within the project “Intelligent Electronic Record of the Operation and Vehicle Performance” whose aim is to develop a voice-operated software for registering the vehicle operation data. The first part (full_noises.zip) consists of relatively long recordings from the vehicle cabin, containing spontaneous speech from the vehicle crew. The recordings are accompanied with detailed transcripts in the Transcriber XML-based format (.trs). Due to the recording settings, the audio contains many different noises, only sparsely interspersed with speech. As such, the set is suitable for robust estimation of the voice activity detector parameters. The second set (prompts.zip) consists of short prompts that were recorded in the controlled setting – the speakers either answered simple questions or they repeated commands and short phrases. The prompts were recorded by 26 different speakers. Each speaker recorded at least two sessions (with identical set of prompts) – first in stationary vehicle, with low level of noise (those recordings are marked by –A_ in the file name) and second while actually driving the car (marked by –B_ or, since several speakers recorded 3 sessions, by –C_). The recordings from this set are suitable mostly for training of the robust domain-specific speech recognizer and also ASR test purposes.

Introduction

This Australian English Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of English speech recognition, spoken language understanding, and conversational AI systems. With 40 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 40 Hours of dual-channel call center conversations between native Australian English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Introduction

Welcome to the Algerian Arabic Scripted Monologue Speech Dataset for the Travel domain, a carefully constructed resource created to support the development of Arabic speech recognition technologies, particularly for applications in travel, tourism, and customer service automation.

Speech Data

This training dataset features 6,000+ high-quality scripted prompt recordings in Algerian Arabic, crafted to simulate real-world Travel industry conversations. It’s ideal for building robust ASR systems, virtual assistants, and customer interaction tools.

Topic Coverage

The dataset includes a wide spectrum of travel-related interactions to reflect diverse real-world scenarios:

Context Elements

To boost contextual realism, the scripted prompts integrate frequently encountered travel terms and variables:

Transcription

Every audio file is paired with a verbatim transcription in .TXT format.

Metadata

Each audio file is enriched with detailed metadata to support advanced analytics and filtering:

Introduction

This Russian Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Russian speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Russian speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Introduction

This Malay Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Malay speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Malay speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Introduction

This Kannada Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Kannada speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Kannada speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Introduction

Introducing the German Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of German language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in German, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

Introduction

Welcome to the Algerian Arabic Scripted Monologue Speech Dataset for the Travel domain, a carefully constructed resource created to support the development of Arabic speech recognition technologies, particularly for applications in travel, tourism, and customer service automation.

Speech Data

This training dataset features 6,000+ high-quality scripted prompt recordings in Algerian Arabic, crafted to simulate real-world Travel industry conversations. It’s ideal for building robust ASR systems, virtual assistants, and customer interaction tools.

Topic Coverage

The dataset includes a wide spectrum of travel-related interactions to reflect diverse real-world scenarios:

Context Elements

To boost contextual realism, the scripted prompts integrate frequently encountered travel terms and variables:

Transcription

Every audio file is paired with a verbatim transcription in .TXT format.

Metadata

Each audio file is enriched with detailed metadata to support advanced analytics and filtering:

Introduction

This Thai Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Thai speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Thai speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases: